





'''simple docstring'''






import argparse

from torch import nn

# transformers_old should correspond to branch `save_old_prophetnet_model_structure` here
# original prophetnet_checkpoints are saved under `patrickvonplaten/..._old` respectively
from transformers_old.modeling_prophetnet import (
    ProphetNetForConditionalGeneration as ProphetNetForConditionalGenerationOld,
)
from transformers_old.modeling_xlm_prophetnet import (
    XLMProphetNetForConditionalGeneration as XLMProphetNetForConditionalGenerationOld,
)

from transformers import ProphetNetForConditionalGeneration, XLMProphetNetForConditionalGeneration, logging


lowerCAmelCase									=   logging.get_logger(__name__)
logging.set_verbosity_info()






def   __A    (				a_    :	str   ,a_    :	str						):
		if "xprophetnet" in prophetnet_checkpoint_path:
				lowerCAmelCase  :     Optional[int]       						= XLMProphetNetForConditionalGenerationOld.from_pretrained(a_						)
				lowerCAmelCase						,		lowerCAmelCase  :     List[Any]       						= XLMProphetNetForConditionalGeneration.from_pretrained(
				    a_   ,output_loading_info=a_						)
		else:
				lowerCAmelCase  :     Union[str, Any]       						= ProphetNetForConditionalGenerationOld.from_pretrained(a_						)
				lowerCAmelCase						,		lowerCAmelCase  :     str       						= ProphetNetForConditionalGeneration.from_pretrained(
				    a_   ,output_loading_info=a_						)

		lowerCAmelCase  :     Optional[int]       						= ["key_proj", "value_proj", "query_proj"]

		lowerCAmelCase  :     List[str]       						= {
		    "self_attn": "ngram_self_attn",
		    "cross_attn": "encoder_attn",
		    "cross_attn_layer_norm": "encoder_attn_layer_norm",
		    "feed_forward_layer_norm": "final_layer_norm",
		    "feed_forward": "",
		    "intermediate": "fc1",
		    "output": "fc2",
		    "key_proj": "k_proj",
		    "query_proj": "q_proj",
		    "value_proj": "v_proj",
		    "word_embeddings": "embed_tokens",
		    "embeddings_layer_norm": "emb_layer_norm",
		    "relative_pos_embeddings": "relative_linear",
		    "ngram_embeddings": "ngram_input_embed",
		    "position_embeddings": "embed_positions",
		}

		for key in loading_info["missing_keys"]:
				lowerCAmelCase  :     Dict       						= key.split("."						)

				if attributes[0] == "lm_head":
						lowerCAmelCase  :     Union[str, Any]       						= prophet
						lowerCAmelCase  :     Dict       						= prophet_old
				else:
						lowerCAmelCase  :     str       						= prophet.prophetnet
						lowerCAmelCase  :     Optional[Any]       						= prophet_old.model

				lowerCAmelCase  :     List[str]       						= False
				for attribute in attributes:
						if attribute in mapping:
								lowerCAmelCase  :     List[str]       						= mapping[attribute]
								if not hasattr(a_   ,a_						) and len(a_						) > 0:
										lowerCAmelCase  :     Union[str, Any]       						= attribute
						elif hasattr(a_   ,a_						):
								lowerCAmelCase  :     Union[str, Any]       						= attribute

						if attribute == "weight":
								assert old_model.weight.shape == model.weight.shape, "Shapes have to match!"
								lowerCAmelCase  :     Tuple       						= old_model.weight
								logger.info(f'''{attribute} is initialized.'''						)
								lowerCAmelCase  :     Union[str, Any]       						= True
								break
						elif attribute == "bias":
								assert old_model.bias.shape == model.bias.shape, "Shapes have to match!"
								lowerCAmelCase  :     Tuple       						= old_model.bias
								logger.info(f'''{attribute} is initialized'''						)
								lowerCAmelCase  :     Tuple       						= True
								break
						elif attribute in special_keys and hasattr(a_   ,"in_proj_weight"						):
								lowerCAmelCase  :     Optional[Any]       						= old_model.in_proj_weight.shape[0] // 3
								lowerCAmelCase  :     Any       						= getattr(a_   ,a_						)
								param.weight.shape == old_model.in_proj_weight[:embed_dim, :].shape, "Shapes have to match"
								param.bias.shape == old_model.in_proj_bias[:embed_dim].shape, "Shapes have to match"
								if attribute == "query_proj":
										lowerCAmelCase  :     Dict       						= nn.Parameter(old_model.in_proj_weight[:embed_dim, :]						)
										lowerCAmelCase  :     str       						= nn.Parameter(old_model.in_proj_bias[:embed_dim]						)

								elif attribute == "key_proj":
										lowerCAmelCase  :     List[Any]       						= nn.Parameter(old_model.in_proj_weight[embed_dim : 2 * embed_dim, :]						)
										lowerCAmelCase  :     int       						= nn.Parameter(old_model.in_proj_bias[embed_dim : 2 * embed_dim]						)
								elif attribute == "value_proj":
										lowerCAmelCase  :     List[str]       						= nn.Parameter(old_model.in_proj_weight[2 * embed_dim :, :]						)
										lowerCAmelCase  :     Optional[int]       						= nn.Parameter(old_model.in_proj_bias[2 * embed_dim :]						)
								lowerCAmelCase  :     Union[str, Any]       						= True
								break
						elif attribute == "position_embeddings":
								assert (
								    model.position_embeddings.weight.shape[-1] == old_model.embed_positions.weight.shape[-1]
								), "Hidden size has to match"
								assert model.position_embeddings.weight.shape[0] == 5_1_2, "We want 512 position_embeddings."
								lowerCAmelCase  :     List[Any]       						= nn.Parameter(old_model.embed_positions.weight[:5_1_2, :]						)
								lowerCAmelCase  :     int       						= True
								break

						if attribute.isdigit():
								lowerCAmelCase  :     Tuple       						= model[int(a_						)]
								lowerCAmelCase  :     int       						= old_model[int(a_						)]
						else:
								lowerCAmelCase  :     Optional[int]       						= getattr(a_   ,a_						)

								if old_attribute == "":
										lowerCAmelCase  :     Any       						= old_model
								else:
										if not hasattr(a_   ,a_						):
												raise ValueError(f'''{old_model} does not have {old_attribute}'''						)
										lowerCAmelCase  :     Dict       						= getattr(a_   ,a_						)

				if not is_key_init:
						raise ValueError(f'''{key} was not correctly initialized!'''						)

		print(f'''Saving model to {pytorch_dump_folder_path}'''						)
		prophet.save_pretrained(a_						)


if __name__ == "__main__":
					lowerCAmelCase									=   argparse.ArgumentParser()
					# Required parameters
					parser.add_argument(
					    """--prophetnet_checkpoint_path""", default=None, type=str, required=True, help="""Path the official PyTorch dump."""
					)
					parser.add_argument(
					    """--pytorch_dump_folder_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
					)
					lowerCAmelCase									=   parser.parse_args()
					convert_prophetnet_checkpoint_to_pytorch(args.prophetnet_checkpoint_path, args.pytorch_dump_folder_path)










'''simple docstring'''






from __future__ import annotations

lowerCAmelCase									=   []






def   __A    (				a_    :	list[list[int]]   ,a_    :	int   ,a_    :	int						):
		for i in range(len(a_						)						):
				if board[row][i] == 1:
						return False
		for i in range(len(a_						)						):
				if board[i][column] == 1:
						return False
		for i, j in zip(range(a_   ,-1   ,-1						)   ,range(a_   ,-1   ,-1						)						):
				if board[i][j] == 1:
						return False
		for i, j in zip(range(a_   ,-1   ,-1						)   ,range(a_   ,len(a_						)						)						):
				if board[i][j] == 1:
						return False
		return True






def   __A    (				a_    :	list[list[int]]   ,a_    :	int						):
		if row >= len(a_						):
				solution.append(a_						)
				printboard(a_						)
				print()
				return True
		for i in range(len(a_						)						):
				if is_safe(a_   ,a_   ,a_						):
						lowerCAmelCase  :     Dict       						= 1
						solve(a_   ,row + 1						)
						lowerCAmelCase  :     Optional[int]       						= 0
		return False






def   __A    (				a_    :	list[list[int]]						):
		for i in range(len(a_						)						):
				for j in range(len(a_						)						):
						if board[i][j] == 1:
								print("Q"   ,end=" "						)
						else:
								print("."   ,end=" "						)
				print()


# n=int(input("The no. of queens"))
lowerCAmelCase									=   8
lowerCAmelCase									=   [[0 for i in range(n)] for j in range(n)]
solve(board, 0)

print("""The total no. of solutions are :""", len(solution))



